AI::XGBoost - Perl wrapper for XGBoost library https://github.com/dmlc/xgboost
version 0.11
use 5.010;
use aliased 'AI::XGBoost::DMatrix';
use AI::XGBoost qw(train);
# We are going to solve a binary classification problem:
# Mushroom poisonous or not
my $train_data = DMatrix->From(file => 'agaricus.txt.train');
my $test_data = DMatrix->From(file => 'agaricus.txt.test');
# With XGBoost we can solve this problem using 'gbtree' booster
# and as loss function a logistic regression 'binary:logistic'
# (Gradient Boosting Regression Tree)
# XGBoost Tree Booster has a lot of parameters that we can tune
# (https://github.com/dmlc/xgboost/blob/master/doc/parameter.md)
my $booster = train(data => $train_data, number_of_rounds => 10, params => {
objective => 'binary:logistic',
eta => 1.0,
max_depth => 2,
silent => 1
});
# For binay classification predictions are probability confidence scores in [0, 1]
# indicating that the label is positive (1 in the first column of agaricus.txt.test)
my $predictions = $booster->predict(data => $test_data);
say join "\n", @$predictions[0 .. 10];
use aliased 'AI::XGBoost::DMatrix';
use AI::XGBoost qw(train);
use Data::Dataset::Classic::Iris;
# We are going to solve a multiple classification problem:
# determining plant species using a set of flower's measures
# XGBoost uses number for "class" so we are going to codify classes
my %class = (
setosa => 0,
versicolor => 1,
virginica => 2
);
my $iris = Data::Dataset::Classic::Iris::get();
# Split train and test, label and features
my $train_dataset = [map {$iris->{$_}} grep {$_ ne 'species'} keys %$iris];
my $test_dataset = [map {$iris->{$_}} grep {$_ ne 'species'} keys %$iris];
sub transpose {
# Transposing without using PDL, Data::Table, Data::Frame or other modules
# to keep minimal dependencies
my $array = shift;
my @aux = ();
for my $row (@$array) {
for my $column (0 .. scalar @$row - 1) {
push @{$aux[$column]}, $row->[$column];
}
}
return \@aux;
}
$train_dataset = transpose($train_dataset);
$test_dataset = transpose($test_dataset);
my $train_label = [map {$class{$_}} @{$iris->{'species'}}];
my $test_label = [map {$class{$_}} @{$iris->{'species'}}];
my $train_data = DMatrix->From(matrix => $train_dataset, label => $train_label);
my $test_data = DMatrix->From(matrix => $test_dataset, label => $test_label);
# Multiclass problems need a diferent objective function and the number
# of classes, in this case we are using 'multi:softprob' and
# num_class => 3
my $booster = train(data => $train_data, number_of_rounds => 20, params => {
max_depth => 3,
eta => 0.3,
silent => 1,
objective => 'multi:softprob',
num_class => 3
});
my $predictions = $booster->predict(data => $test_data);Perl wrapper for XGBoost library.
The easiest way to use the wrapper is using train, but beforehand
you need the data to be used contained in a DMatrix object
This is a work in progress, feedback, comments, issues, suggestion and pull requests are welcome!!
XGBoost library is used via Alien::XGBoost. That means downloading, compiling and installing if it's not available in your system.
Performs gradient boosting using the data and parameters passed
Returns a trained AI::XGBoost::Booster used
-
params
Parameters for the booster object.
Full list available: https://github.com/dmlc/xgboost/blob/master/doc/parameter.md
-
data
AI::XGBoost::DMatrix object used for training
-
number_of_rounds
Number of boosting iterations
The goal is to make a full wrapper for XGBoost.
-
0.2
Full C API "easy" to use, with PDL support as AI::XGBoost::CAPI
Easy means clients don't have to use FFI::Platypus or modules dealing with C structures
-
0.25
Alien package for libxgboost.so/xgboost.dll
-
0.3
Object oriented API Moose based with DMatrix and Booster classes
-
0.4
Complete object oriented API
-
0.5
Use perl signatures (https://metacpan.org/pod/distribution/perl/pod/perlexperiment.pod#Subroutine-signatures)
Pablo Rodríguez González pablo.rodriguez.gonzalez@gmail.com
Copyright (c) 2017 by Pablo Rodríguez González.
Ruben me@ruben.tech